Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory135.8 KiB
Average record size in memory278.0 B

Variable types

Text1
Numeric7
Categorical6
DateTime1
Boolean4

Alerts

Annual_Income is highly overall correlated with ClusterHigh correlation
Cluster is highly overall correlated with Annual_IncomeHigh correlation
Gender is highly overall correlated with Gender_EncodedHigh correlation
Gender_Encoded is highly overall correlated with GenderHigh correlation
Membership_Encoded is highly overall correlated with Membership_StatusHigh correlation
Membership_Status is highly overall correlated with Membership_EncodedHigh correlation
Purchase_Amount is highly overall correlated with Z_Purchase_AmountHigh correlation
Z_Purchase_Amount is highly overall correlated with Purchase_AmountHigh correlation
Customer_ID has unique values Unique
Annual_Income has unique values Unique

Reproduction

Analysis started2025-04-14 12:57:53.745732
Analysis finished2025-04-14 12:58:00.757351
Duration7.01 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Customer_ID
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
2025-04-14T12:58:01.085150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowCUST0000
2nd rowCUST0001
3rd rowCUST0002
4th rowCUST0003
5th rowCUST0004
ValueCountFrequency (%)
cust0004 1
 
0.2%
cust0499 1
 
0.2%
cust0000 1
 
0.2%
cust0001 1
 
0.2%
cust0484 1
 
0.2%
cust0485 1
 
0.2%
cust0486 1
 
0.2%
cust0487 1
 
0.2%
cust0488 1
 
0.2%
cust0489 1
 
0.2%
Other values (490) 490
98.0%
2025-04-14T12:58:01.532570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 700
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
4 200
 
5.0%
1 200
 
5.0%
2 200
 
5.0%
3 200
 
5.0%
9 100
 
2.5%
Other values (4) 400
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 700
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
4 200
 
5.0%
1 200
 
5.0%
2 200
 
5.0%
3 200
 
5.0%
9 100
 
2.5%
Other values (4) 400
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 700
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
4 200
 
5.0%
1 200
 
5.0%
2 200
 
5.0%
3 200
 
5.0%
9 100
 
2.5%
Other values (4) 400
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 700
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
4 200
 
5.0%
1 200
 
5.0%
2 200
 
5.0%
3 200
 
5.0%
9 100
 
2.5%
Other values (4) 400
10.0%

Age
Real number (ℝ)

Distinct52
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.22
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-04-14T12:58:01.670398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q132
median45
Q357
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.036082
Coefficient of variation (CV)0.340029
Kurtosis-1.1069905
Mean44.22
Median Absolute Deviation (MAD)12
Skewness-0.11270863
Sum22110
Variance226.08377
MonotonicityNot monotonic
2025-04-14T12:58:01.804807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 18
 
3.6%
52 16
 
3.2%
49 15
 
3.0%
41 15
 
3.0%
61 14
 
2.8%
19 14
 
2.8%
69 14
 
2.8%
56 13
 
2.6%
45 13
 
2.6%
42 12
 
2.4%
Other values (42) 356
71.2%
ValueCountFrequency (%)
18 12
2.4%
19 14
2.8%
20 11
2.2%
21 8
1.6%
22 6
1.2%
23 9
1.8%
24 9
1.8%
25 12
2.4%
26 10
2.0%
27 1
 
0.2%
ValueCountFrequency (%)
69 14
2.8%
68 10
2.0%
67 6
1.2%
66 12
2.4%
65 12
2.4%
64 9
1.8%
63 6
1.2%
62 9
1.8%
61 14
2.8%
60 4
 
0.8%

Gender
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size30.4 KiB
Male
188 
Female
159 
Other
153 

Length

Max length6
Median length5
Mean length4.942
Min length4

Characters and Unicode

Total characters2471
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowOther
3rd rowOther
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 188
37.6%
Female 159
31.8%
Other 153
30.6%

Length

2025-04-14T12:58:01.943482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:02.029029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 188
37.6%
female 159
31.8%
other 153
30.6%

Most occurring characters

ValueCountFrequency (%)
e 659
26.7%
a 347
14.0%
l 347
14.0%
M 188
 
7.6%
F 159
 
6.4%
m 159
 
6.4%
O 153
 
6.2%
t 153
 
6.2%
h 153
 
6.2%
r 153
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 659
26.7%
a 347
14.0%
l 347
14.0%
M 188
 
7.6%
F 159
 
6.4%
m 159
 
6.4%
O 153
 
6.2%
t 153
 
6.2%
h 153
 
6.2%
r 153
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 659
26.7%
a 347
14.0%
l 347
14.0%
M 188
 
7.6%
F 159
 
6.4%
m 159
 
6.4%
O 153
 
6.2%
t 153
 
6.2%
h 153
 
6.2%
r 153
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 659
26.7%
a 347
14.0%
l 347
14.0%
M 188
 
7.6%
F 159
 
6.4%
m 159
 
6.4%
O 153
 
6.2%
t 153
 
6.2%
h 153
 
6.2%
r 153
 
6.2%

Annual_Income
Real number (ℝ)

High correlation  Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85364.862
Minimum20077
Maximum149948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-04-14T12:58:02.133435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20077
5-th percentile24419.95
Q150418.5
median87475
Q3120223.5
95-th percentile145887.6
Maximum149948
Range129871
Interquartile range (IQR)69805

Descriptive statistics

Standard deviation39127.187
Coefficient of variation (CV)0.45835237
Kurtosis-1.2290321
Mean85364.862
Median Absolute Deviation (MAD)33998
Skewness-0.016726739
Sum42682431
Variance1.5309367 × 109
MonotonicityNot monotonic
2025-04-14T12:58:02.279141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80500 1
 
0.2%
102152 1
 
0.2%
145036 1
 
0.2%
144049 1
 
0.2%
32175 1
 
0.2%
104458 1
 
0.2%
133453 1
 
0.2%
80535 1
 
0.2%
95039 1
 
0.2%
51821 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20077 1
0.2%
20117 1
0.2%
20126 1
0.2%
20235 1
0.2%
20814 1
0.2%
20846 1
0.2%
20922 1
0.2%
21342 1
0.2%
21605 1
0.2%
21645 1
0.2%
ValueCountFrequency (%)
149948 1
0.2%
149922 1
0.2%
149597 1
0.2%
149038 1
0.2%
149028 1
0.2%
148876 1
0.2%
148644 1
0.2%
148177 1
0.2%
147978 1
0.2%
147796 1
0.2%

Spending_Score
Real number (ℝ)

Distinct101
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.472
Minimum0
Maximum100
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-04-14T12:58:02.417459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median49.5
Q376.25
95-th percentile97
Maximum100
Range100
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation29.724608
Coefficient of variation (CV)0.58893264
Kurtosis-1.2325387
Mean50.472
Median Absolute Deviation (MAD)25.5
Skewness0.058069184
Sum25236
Variance883.55232
MonotonicityNot monotonic
2025-04-14T12:58:02.558587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 17
 
3.4%
98 11
 
2.2%
58 11
 
2.2%
24 10
 
2.0%
28 10
 
2.0%
39 10
 
2.0%
16 10
 
2.0%
61 9
 
1.8%
86 9
 
1.8%
38 9
 
1.8%
Other values (91) 394
78.8%
ValueCountFrequency (%)
0 2
 
0.4%
1 4
0.8%
2 5
1.0%
3 4
0.8%
4 7
1.4%
5 4
0.8%
6 4
0.8%
7 1
 
0.2%
8 6
1.2%
9 5
1.0%
ValueCountFrequency (%)
100 5
 
1.0%
99 4
 
0.8%
98 11
2.2%
97 17
3.4%
96 1
 
0.2%
95 6
 
1.2%
94 3
 
0.6%
93 5
 
1.0%
92 7
1.4%
91 3
 
0.6%

Purchase_Amount
Real number (ℝ)

High correlation 

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.09406
Minimum14.89
Maximum999.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-04-14T12:58:02.692482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.89
5-th percentile66.2665
Q1243.3425
median505.72
Q3733.5725
95-th percentile956.074
Maximum999.42
Range984.53
Interquartile range (IQR)490.23

Descriptive statistics

Standard deviation286.51389
Coefficient of variation (CV)0.56950363
Kurtosis-1.1917615
Mean503.09406
Median Absolute Deviation (MAD)251.545
Skewness-0.0053721663
Sum251547.03
Variance82090.211
MonotonicityNot monotonic
2025-04-14T12:58:02.825838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
245.17 2
 
0.4%
869.59 2
 
0.4%
841.46 1
 
0.2%
416.92 1
 
0.2%
614.87 1
 
0.2%
219.92 1
 
0.2%
235.8 1
 
0.2%
885.36 1
 
0.2%
821 1
 
0.2%
605.6 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
14.89 1
0.2%
15.7 1
0.2%
17.49 1
0.2%
19.67 1
0.2%
21.5 1
0.2%
28.05 1
0.2%
33.4 1
0.2%
35.39 1
0.2%
35.55 1
0.2%
39.67 1
0.2%
ValueCountFrequency (%)
999.42 1
0.2%
996.37 1
0.2%
994.2 1
0.2%
992.56 1
0.2%
992.24 1
0.2%
991.34 1
0.2%
987.91 1
0.2%
987.77 1
0.2%
986.8 1
0.2%
986.76 1
0.2%

Transaction_Frequency
Real number (ℝ)

Distinct49
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.338
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-04-14T12:58:03.246943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median24
Q337
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.717438
Coefficient of variation (CV)0.54137807
Kurtosis-1.1338052
Mean25.338
Median Absolute Deviation (MAD)12
Skewness0.0091648999
Sum12669
Variance188.16809
MonotonicityNot monotonic
2025-04-14T12:58:03.384943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
21 24
 
4.8%
4 14
 
2.8%
24 14
 
2.8%
37 13
 
2.6%
17 13
 
2.6%
22 13
 
2.6%
49 13
 
2.6%
39 12
 
2.4%
36 12
 
2.4%
32 12
 
2.4%
Other values (39) 360
72.0%
ValueCountFrequency (%)
1 7
1.4%
2 6
1.2%
3 6
1.2%
4 14
2.8%
5 11
2.2%
6 10
2.0%
7 10
2.0%
8 9
1.8%
9 9
1.8%
10 11
2.2%
ValueCountFrequency (%)
49 13
2.6%
48 7
1.4%
47 8
1.6%
46 11
2.2%
45 11
2.2%
44 10
2.0%
43 9
1.8%
42 10
2.0%
41 10
2.0%
40 11
2.2%

Membership_Status
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size30.8 KiB
Platinum
142 
Gold
122 
Basic
120 
Silver
116 

Length

Max length8
Median length6
Mean length5.84
Min length4

Characters and Unicode

Total characters2920
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilver
2nd rowBasic
3rd rowSilver
4th rowBasic
5th rowBasic

Common Values

ValueCountFrequency (%)
Platinum 142
28.4%
Gold 122
24.4%
Basic 120
24.0%
Silver 116
23.2%

Length

2025-04-14T12:58:03.531873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:03.670951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
platinum 142
28.4%
gold 122
24.4%
basic 120
24.0%
silver 116
23.2%

Most occurring characters

ValueCountFrequency (%)
l 380
13.0%
i 378
 
12.9%
a 262
 
9.0%
P 142
 
4.9%
t 142
 
4.9%
n 142
 
4.9%
u 142
 
4.9%
m 142
 
4.9%
G 122
 
4.2%
o 122
 
4.2%
Other values (8) 946
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 380
13.0%
i 378
 
12.9%
a 262
 
9.0%
P 142
 
4.9%
t 142
 
4.9%
n 142
 
4.9%
u 142
 
4.9%
m 142
 
4.9%
G 122
 
4.2%
o 122
 
4.2%
Other values (8) 946
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 380
13.0%
i 378
 
12.9%
a 262
 
9.0%
P 142
 
4.9%
t 142
 
4.9%
n 142
 
4.9%
u 142
 
4.9%
m 142
 
4.9%
G 122
 
4.2%
o 122
 
4.2%
Other values (8) 946
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 380
13.0%
i 378
 
12.9%
a 262
 
9.0%
P 142
 
4.9%
t 142
 
4.9%
n 142
 
4.9%
u 142
 
4.9%
m 142
 
4.9%
G 122
 
4.2%
o 122
 
4.2%
Other values (8) 946
32.4%
Distinct360
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-04-14 00:00:00
Maximum2025-04-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T12:58:03.842500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:58:04.059833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Z_Purchase_Amount
Real number (ℝ)

High correlation 

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.9737992 × 10-17
Minimum-1.705652
Maximum1.7340276
Zeros0
Zeros (%)0.0%
Negative248
Negative (%)49.6%
Memory size4.0 KiB
2025-04-14T12:58:04.268276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.705652
5-th percentile-1.5261565
Q1-0.9075012
median0.0091743191
Q30.80522889
95-th percentile1.5825885
Maximum1.7340276
Range3.4396797
Interquartile range (IQR)1.7127301

Descriptive statistics

Standard deviation1.0010015
Coefficient of variation (CV)-2.0125491 × 1016
Kurtosis-1.1917615
Mean-4.9737992 × 10-17
Median Absolute Deviation (MAD)0.87882971
Skewness-0.0053721663
Sum-1.7319479 × 10-14
Variance1.002004
MonotonicityNot monotonic
2025-04-14T12:58:04.467135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9011164125 2
 
0.4%
1.280436989 2
 
0.4%
1.182158431 1
 
0.2%
-0.3010686936 1
 
0.2%
0.3905146889 1
 
0.2%
-0.989333035 1
 
0.2%
-0.9338526403 1
 
0.2%
1.335533073 1
 
0.2%
1.110676763 1
 
0.2%
0.3581278339 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
-1.705652009 1
0.2%
-1.70282209 1
0.2%
-1.696568317 1
0.2%
-1.688951991 1
0.2%
-1.68255847 1
0.2%
-1.659674554 1
0.2%
-1.640983111 1
0.2%
-1.634030593 1
0.2%
-1.633471596 1
0.2%
-1.619077439 1
0.2%
ValueCountFrequency (%)
1.734027646 1
0.2%
1.723371776 1
0.2%
1.715790387 1
0.2%
1.710060674 1
0.2%
1.708942681 1
0.2%
1.705798326 1
0.2%
1.693814841 1
0.2%
1.693325719 1
0.2%
1.689936803 1
0.2%
1.689797054 1
0.2%

Gender_Encoded
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
1
188 
0
159 
2
153 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Length

2025-04-14T12:58:04.632160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:04.725951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Most occurring characters

ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 188
37.6%
0 159
31.8%
2 153
30.6%

Membership_Encoded
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2
142 
1
122 
0
120 
3
116 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%

Length

2025-04-14T12:58:04.839043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:04.936067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%

Most occurring characters

ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 142
28.4%
1 122
24.4%
0 120
24.0%
3 116
23.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
412 
True
88 
ValueCountFrequency (%)
False 412
82.4%
True 88
 
17.6%
2025-04-14T12:58:05.036245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
398 
True
102 
ValueCountFrequency (%)
False 398
79.6%
True 102
 
20.4%
2025-04-14T12:58:05.103249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
396 
True
104 
ValueCountFrequency (%)
False 396
79.2%
True 104
 
20.8%
2025-04-14T12:58:05.171810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
390 
True
110 
ValueCountFrequency (%)
False 390
78.0%
True 110
 
22.0%
2025-04-14T12:58:05.250326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Cluster
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2
140 
1
130 
0
124 
3
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Length

2025-04-14T12:58:05.369985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:05.472663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Most occurring characters

ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 140
28.0%
1 130
26.0%
0 124
24.8%
3 106
21.2%

Purchase_Month
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.458
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-04-14T12:58:05.598429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4825986
Coefficient of variation (CV)0.53926891
Kurtosis-1.2063735
Mean6.458
Median Absolute Deviation (MAD)3
Skewness0.032647111
Sum3229
Variance12.128493
MonotonicityNot monotonic
2025-04-14T12:58:05.739042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 49
9.8%
8 47
9.4%
9 45
9.0%
4 44
8.8%
2 43
8.6%
1 43
8.6%
3 42
8.4%
5 40
8.0%
7 40
8.0%
6 39
7.8%
Other values (2) 68
13.6%
ValueCountFrequency (%)
1 43
8.6%
2 43
8.6%
3 42
8.4%
4 44
8.8%
5 40
8.0%
6 39
7.8%
7 40
8.0%
8 47
9.4%
9 45
9.0%
10 33
6.6%
ValueCountFrequency (%)
12 49
9.8%
11 35
7.0%
10 33
6.6%
9 45
9.0%
8 47
9.4%
7 40
8.0%
6 39
7.8%
5 40
8.0%
4 44
8.8%
3 42
8.4%

Purchase_Year
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
2024
255 
2023
175 
2025
70 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2025
4th row2023
5th row2025

Common Values

ValueCountFrequency (%)
2024 255
51.0%
2023 175
35.0%
2025 70
 
14.0%

Length

2025-04-14T12:58:05.894590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T12:58:06.004505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024 255
51.0%
2023 175
35.0%
2025 70
 
14.0%

Most occurring characters

ValueCountFrequency (%)
2 1000
50.0%
0 500
25.0%
4 255
 
12.8%
3 175
 
8.8%
5 70
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1000
50.0%
0 500
25.0%
4 255
 
12.8%
3 175
 
8.8%
5 70
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1000
50.0%
0 500
25.0%
4 255
 
12.8%
3 175
 
8.8%
5 70
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1000
50.0%
0 500
25.0%
4 255
 
12.8%
3 175
 
8.8%
5 70
 
3.5%

Interactions

2025-04-14T12:57:59.680020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:54.850477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.022483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.723649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.396110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.066966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.975688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.775123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:54.941574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.118800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.823759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.489998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.160400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.068900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.891721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:55.038971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.215977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.923955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.583555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.262631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.171976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.986057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:55.129070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.321843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.009055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.676678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.362684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.280872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:58:00.079459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:55.231522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.416839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.107687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.767574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.459820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.377866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:58:00.181653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:55.323366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.513704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.204450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.876887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.554718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.477271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:58:00.284329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:55.929901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:56.625058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.307827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:57.972584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:58.650328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T12:57:59.583771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-14T12:58:06.102557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAnnual_IncomeCategory_Purchased_ClothingCategory_Purchased_ElectronicsCategory_Purchased_GroceriesCategory_Purchased_Home DecorClusterGenderGender_EncodedMembership_EncodedMembership_StatusPurchase_AmountPurchase_MonthPurchase_YearSpending_ScoreTransaction_FrequencyZ_Purchase_Amount
Age1.000-0.0600.0640.1100.0000.0000.0000.0650.0650.0270.0270.008-0.0230.000-0.014-0.0130.008
Annual_Income-0.0601.0000.0000.0000.0000.0000.9100.0000.0000.0690.069-0.0580.0430.015-0.0480.029-0.058
Category_Purchased_Clothing0.0640.0001.0000.2230.2260.2350.0000.0000.0000.0360.0360.0000.0000.0000.0000.0000.000
Category_Purchased_Electronics0.1100.0000.2231.0000.2500.2590.0000.0000.0000.0170.0170.0000.0000.0720.1470.0160.000
Category_Purchased_Groceries0.0000.0000.2260.2501.0000.2630.0000.0470.0470.0480.0480.0830.0000.0540.0000.0990.083
Category_Purchased_Home Decor0.0000.0000.2350.2590.2631.0000.0000.0000.0000.0000.0000.0380.0870.0000.0000.0710.038
Cluster0.0000.9100.0000.0000.0000.0001.0000.0000.0000.0560.0560.0730.0640.0000.0000.0590.073
Gender0.0650.0000.0000.0000.0470.0000.0001.0001.0000.0000.0000.0550.1050.0900.1040.0000.055
Gender_Encoded0.0650.0000.0000.0000.0470.0000.0001.0001.0000.0000.0000.0550.1050.0900.1040.0000.055
Membership_Encoded0.0270.0690.0360.0170.0480.0000.0560.0000.0001.0001.0000.0000.0330.0170.0000.0000.000
Membership_Status0.0270.0690.0360.0170.0480.0000.0560.0000.0001.0001.0000.0000.0330.0170.0000.0000.000
Purchase_Amount0.008-0.0580.0000.0000.0830.0380.0730.0550.0550.0000.0001.000-0.0350.0000.0550.0601.000
Purchase_Month-0.0230.0430.0000.0000.0000.0870.0640.1050.1050.0330.033-0.0351.0000.475-0.0140.007-0.035
Purchase_Year0.0000.0150.0000.0720.0540.0000.0000.0900.0900.0170.0170.0000.4751.0000.0000.1280.000
Spending_Score-0.014-0.0480.0000.1470.0000.0000.0000.1040.1040.0000.0000.055-0.0140.0001.000-0.0300.055
Transaction_Frequency-0.0130.0290.0000.0160.0990.0710.0590.0000.0000.0000.0000.0600.0070.128-0.0301.0000.060
Z_Purchase_Amount0.008-0.0580.0000.0000.0830.0380.0730.0550.0550.0000.0001.000-0.0350.0000.0550.0601.000

Missing values

2025-04-14T12:58:00.458761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T12:58:00.637167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_IDAgeGenderAnnual_IncomeSpending_ScorePurchase_AmountTransaction_FrequencyMembership_StatusPurchase_DateZ_Purchase_AmountGender_EncodedMembership_EncodedCategory_Purchased_ClothingCategory_Purchased_ElectronicsCategory_Purchased_GroceriesCategory_Purchased_Home DecorClusterPurchase_MonthPurchase_Year
0CUST000056Male10215218228.5611Silver2024-05-28-0.95914713FalseFalseFalseTrue152024
1CUST000169Other1450361891.5631Basic2024-02-03-1.43778820FalseTrueFalseFalse222024
2CUST000246Other14404935683.6915Silver2025-01-310.63095323TrueFalseFalseFalse212025
3CUST000332Male4673428657.979Basic2023-12-130.54109410FalseFalseTrueFalse0122023
4CUST000460Female2637159280.5323Basic2025-03-11-0.77757800FalseTrueFalseFalse032025
5CUST000525Male13889481951.3519Silver2023-11-171.56608413FalseTrueFalseFalse2112023
6CUST000638Other460691159.559Silver2023-04-14-1.20024923FalseFalseFalseTrue042023
7CUST000756Other999050438.0135Platinum2023-07-10-0.22738622FalseTrueFalseFalse172023
8CUST000836Female3291046944.1821Basic2023-12-241.54103400FalseTrueFalseFalse0122023
9CUST000940Other9347968425.539Silver2024-03-04-0.27098823FalseTrueFalseFalse132024
Customer_IDAgeGenderAnnual_IncomeSpending_ScorePurchase_AmountTransaction_FrequencyMembership_StatusPurchase_DateZ_Purchase_AmountGender_EncodedMembership_EncodedCategory_Purchased_ClothingCategory_Purchased_ElectronicsCategory_Purchased_GroceriesCategory_Purchased_Home DecorClusterPurchase_MonthPurchase_Year
490CUST049018Other13337428114.6143Basic2025-02-01-1.35725720FalseFalseFalseTrue222025
491CUST049118Female863583644.4241Silver2023-04-220.49375403TrueFalseFalseFalse142023
492CUST049264Female3732780223.8839Platinum2024-06-14-0.97549802FalseFalseFalseTrue062024
493CUST049351Female6705798623.3922Gold2024-08-120.42028101FalseFalseFalseTrue382024
494CUST049449Male2615012653.7049Platinum2024-07-090.52617612TrueFalseFalseFalse072024
495CUST049565Male2442538160.5017Platinum2023-10-09-1.19693012FalseTrueFalseFalse0102023
496CUST049642Male1436541470.7434Basic2024-04-04-1.51052710FalseFalseFalseFalse242024
497CUST049757Male3603228782.956Silver2023-08-250.97774013TrueFalseFalseFalse082023
498CUST049862Female2219828465.2046Gold2024-10-21-0.13239201FalseFalseTrueFalse0102024
499CUST049918Other805007467.586Basic2023-12-11-1.52156820FalseTrueFalseFalse3122023